Here is a 2800+ word blog post on Azure Performance Monitoring:
An In-Depth Guide to Monitoring for Optimal Performance on Azure
Cloud environments introduce complexity when ensuring strong application performance. With so many abstracted infrastructure components, tracking down issues can feel like chasing a ghost.
In this comprehensive guide, we’ll cover proven methods for monitoring Azure to keep your critical cloud workloads running smoothly.
Why Azure Performance Monitoring Matters
Performance issues directly impact user experience and business revenue, especially for mission-critical workloads running on Azure infrastructure components like virtual machines, databases, Kubernetes, and serverless.
While Azure provides native redundancy for high availability, production applications still degrade or fail from common problems like:
- Resource exhaustion from unexpected traffic spikes
- Cascading component failures through intricate dependencies
- Transient network blips disrupting connectivity
- Back-end processing delays accumulating into long response times
By proactively monitoring key performance metrics, you can quickly detect degradations and failures to troubleshoot the root cause before major impact.
Just as importantly, monitoring helps you right-size resource allocations to avoid overspending while still meeting SLAs.
Key Azure Resources to Monitor
Azure allows you to build complex architectures — from IaaS VMs to PaaS databases to serverless functions.
While each resource will have specific metrics, several core measurements indicate general performance regardless of workload type or architecture.
Virtual Machines
- CPU % utilization per VM and instance size
- Memory utilization % per VM
- Disk throughput bandwidth Mb/s per disk
- Network ingress/egress traffic Mb/s per NIC
Kubernetes Services
- CPU/Memory usage by pod, controller, namespace, cluster
- Pods ready vs requested ratio
- Restart rate per pod
- API server latency in ms
- Node disk throughput Mb/s
Databases
- CPU % utilization
- Memory utilization %
- Storage utilization %
- IOPS or throughput
- Concurrent connections
- Database transaction latency
App Services
- Average memory working set MB
- CPU % utilization
- Http queue length
- Current connections
- API calls count
- HTTP errors
Functions & Serverless
- Execution count
- Execution time ms
- Function duration async
- Success count or errors
Understanding typical values for these metrics will help you quickly detect anomalies indicative of emerging issues.
Designing an Azure Monitoring Strategy
While Azure provides basic platform metrics, this visibility remains limited for troubleshooting. Designing an effective monitoring strategy requires more advanced capabilities:
Comprehensive Metrics
Combine Azure platform metrics with application performance data from all components. For example, measure backend processing delays contributing to poor frontend response times.
Unified Dashboard
Correlate metrics across various backends and infrastructure in a single pane for quicker troubleshooting. Avoid hopping between different consoles.
Smart Alerting
Set dynamic thresholds vs static absolute values. For example, trigger CPU alerts if utilization exceeds 70% for 15 minutes vs just 80% alone.
Trace Requests End-to-End
Follow a request downstream across all infrastructure to pinpoint slowdowns and failures by component.
Proactive notifications
Notify appropriate teams via email, SMS or chat bots when errors threaten SLAs.
Contextual Logging
Centralize logging with metadata across hosts for filtering and reporting.
Visualize Dependencies
Map out component communication channels and transmission volumes to spot bottlenecks.
Performance Benchmarking
Compare current load testing metrics vs prior releases to avoid regressions.
simulate load
Replay production traffic against pre-production to validate capacity planning.
Root Cause Analysis
Determine failure domino chain and components impacted automatically using advanced analytics.
Azure Native Monitoring Tools
Azure provides robust native tools for basic infrastructure and application monitoring without added costs:
Azure Monitor
Consolidates metrics, logs, and transactions across cloud and on-prem components for centralized analysis and debugging. Offers deep integration with Azure services via API for custom reporting.
Application Insights
Enables rich application performance monitoring for REST APIs and common app frameworks (.NET, Java, Node.js) hosted on Azure or other clouds. Provides distributed transaction tracing to dissect request journeys. Integrates with Azure Monitor workbook for customized reporting.
Log Analytics
Performs complex querying and analytics against log data from any source. Enables visualizing statistics, correlations, and trends across logs.
Azure Diagnostic Logs
Captures granular VM operating system and Azure resource logs for troubleshooting.
Azure Advisor
Scans deployments and makes recommendations for reliability, security, operational excellence, cost and performance.
Advance Azure Monitoring Capabilities
While native tools cover baseline infrastructure monitoring, multiple vendors offer enhanced capabilities on the Azure marketplace for cloud-scale production needs:
Unified Visibility
Holistic monitoring across entire hybrid technology stack – Azure environments, Kubernetes, private clouds, containers, VMs, app servers, databases, custom applications.
Smart Alerting
Get notified for performance anomalies most likely causing downstream impact vs all breaches. Leverage techniques like baselining, anomaly detection using AI, and dynamic thresholds.
Diagnostics Acceleration
Machine learning detects root cause of problems automatically by analyzing inter-component dependencies. Also identifies components affected by an outage.
Distributed Tracing
Follows transactions end-to-end through complex, distributed architectures by injecting software intelligence.
Capacity Planning
Right sizes deployments by forecasting cloud consumption and estimating growth requirements. Recommends optimal instance types and numbers given workload profiles.
Cloud Automation
Scales deployments up or down based on usage. For example, auto scale out Kubernetes pods or serverless functions to meet demand spikes or reduce weekends. Saves manual tuning effort.
Designing Monitoring Dashboards
An intuitive dashboard that consolidates various metrics in a single pane enables faster troubleshooting.
Here are some best practices for effective dashboard design:
Summarize Key Metrics
Highlight the most important KPIs and SLIs on the top like application response time, error rate %, availability %. Help users quickly spot anomalies.
Break Down by Component
Add sections for metrics per component like frontend, backend, cache layer, database etc. Make it easy to isolate problem areas.
Compare Business Transactions
Measure user journeys across key workflows to compare (eg. checkout, search). Uncover transactional bottlenecks.
Contextual Logging
Show most recent error logs inline to aid in diagnostics.
Visualize Infrastructure Dependencies
Maps to highlight component communication channels and volumes help identify cascading failures.
Customizable Layouts
Arrange widgets freely based on preferences. Save and share layouts.
Access Control
Limit visibility by user roles – developers, product owners, CTOs etc.
Setting Smart Alert Rules
The real power of monitoring lies in alerts triggered proactively during anomalous conditions — before it causes perceivable business impact.
Here are key considerations for configuring smart alert rules:
Leverage Dynamic Thresholds
Set CPU usage alerts at 60% for 1 minute vs. 80% alone to detect transient spikes.
Focus on Key Performance Metrics
Alert on backend error rate % increase vs. every single occurrence to avoid notification fatigue.
Monitor Critical User Journeys
Measure checkout completion % dropping vs. homepage latency alone.
Schedule Different Notifications
Trigger SMS alerts at night for priority issues vs. email during the day.
Notify Relevant Teams
Configure to reach backend developers vs. deployment engineers based on context.
Suppress During Maintenance
Prevent expected alerts when infrastructure is intentionally taken down.
Remember Time Lags
Account for data aggregation delays so alerts fire at start of issues.
Assess Frequency & Duration
Warning on 2 failures/hour lasting > 1 minute vs. single occurrence.
Conclusion
With cloud environments consisting of abstracted building blocks, monitoring for performance bottlenecks becomes non-intuitive. By instrumenting key metrics across the technology stack and setting smart alerts, IT teams can stay one step ahead.
Choose solutions that provide unified visibility, alerting, distributed tracing and advanced troubleshooting capabilities across your entire hybrid environment. And focus visibility on business transactions end-users care about.
With the right monitoring strategy powered by today‘s solutions, your IT organization can confidently embrace cloud agility without compromising reliability.
